Examples of using Density estimation in English and their translations into Russian
{-}
-
Official
-
Colloquial
In C++, libagf is a library for variable kernel density estimation.
This approach is known as kernel density estimation or the Parzen window technique.
Kernel density estimation provides better estimates of the density than histograms.
The construction of a kernel density estimate finds interpretations in fields outside of density estimation.
In Julia, kernel density estimation is implemented in the KernelDensity. ji package.
The Subgroup recommended an assessment of the utility of wideband(frequency modulated echosounders)for krill identification and density estimation.
In MATLAB, kernel density estimation is implemented through the ksdensity function Statistics Toolbox.
It is used to find a lower bound on the error probability of any decoder as well as the lower bounds for minimax risks in density estimation.
In Octave, kernel density estimation is implemented by the kernel_density option econometrics package.
It has been successfully used on both supervised learning tasks(regression, classification and distance learning) andunsupervised learning density estimation.
In IGOR Pro, kernel density estimation is implemented by the StatsKDE operation added in Igor Pro 7.00.
LOF shares some concepts with DBSCAN and OPTICS such as the concepts of"core distance" and"reachability distance",which are used for local density estimation.
In the NAG Library, kernel density estimation is implemented via the g10ba routine available in both the Fortran and the C versions of the Library.
Scikit-learn Numpy/Python implementation uses ball tree for efficient neighboring points lookup DBSCAN OPTICS algorithm Kernel density estimation(KDE) Kernel(statistics) Cheng, Yizong August 1995.
However, target identification methods and density estimation will be dependent on the target species and require further discussion.
For an implementation of the kde.R function, which does not require installing any packages or libraries, see kde.R. The btb library,dedicated to urban analysis, implements kernel density estimation through kernel_smoothing.
Several algorithms with papers on Fast kernel density estimation, Improved Fast Gauss Transformation, and Fast ranking.
Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.
In Minitab, the Royal Society of Chemistry has created a macro to run kernel density estimation based on their Analytical Methods Committee Technical Brief 4.
In CrimeStat, kernel density estimation is implemented using five different kernel functions- normal, uniform, quartic, negative exponential, and triangular.
In Excel, the Royal Society of Chemistry has created an add-in to run kernel density estimation based on their Analytical Methods Committee Technical Brief 4.
Kernel density estimation is also used in interpolating a Head Bang routine, in estimating a two-dimensional Journey-to-crime density function, and in estimating a three-dimensional Bayesian Journey-to-crime estimate.
The Subgroup recommended that the swarm-based approach should be used for krill density estimation from data collected along transects by krill fishing vessels following the procedure agreed below paragraphs 3.4 to 3.18.
If the bandwidth is not held fixed, but is varied depending upon the location of either the estimate(balloon estimator) or the samples(pointwise estimator), this produces a particularly powerful method termed adaptive orvariable bandwidth kernel density estimation.
In gnuplot, kernel density estimation is implemented by the smooth kdensity option, the datafile can contain a weight and bandwidth for each point, or the bandwidth can be set automatically according to"Silverman's rule of thumb" see above.
In R, it is implemented through density in the base distribution, bkde in the KernSmooth library,ParetoDensityEstimation in the AdaptGauss library(for pareto distribution density estimation), kde in the ks library, dkden and dbckden in the evmix library(latter for boundary corrected kernel density estimation for bounded support), npudens in the np library(numeric and categorical data), sm. density in the sm library.
In Mathematica, numeric kernel density estimation is implemented by the function SmoothKernelDistribution here and symbolic estimation is implemented using the function KernelMixtureDistribution here both of which provide data-driven bandwidths.
Data processing procedure for krill density estimation 5.1 SG-ASAM noted the WG-EMM-15 discussion(SC-CAMLR-XXXIV, Annex 6, paragraph 2.59) that highlighted difficulties following the CCAMLR biomass estimation procedure because information was distributed over several years of SG-ASAM meetings.
The definition of density("Mass and inertia", 2)allows estimation of the average magnetic flux density inside the particles.
Estimation of Total Biomass from Biomass Density.